How a Distributed Data Engines Enables Massive Operational Data Analytics
As mentioned in a previous blog 5 Reasons Why You Need a DATA ENGINE, a data engine is like a high-powered machine that processes raw data and converts it into a usable form that can be analyzed and interpreted by humans or other systems. It's similar to a car engine, which takes in fuel and converts it into energy that propels the car forward.
For example, imagine you work for a manufacturing company that produces a large number of products every day. Each product has its own unique identification number, and the company wants to track how many of each product they're producing every hour. To do this, they need to collect data from each production line and organize it in a way that's easy to understand.
A data engine can help with this by taking in the raw data from each production line and performing various operations on it. It might clean and validate the data to ensure it's accurate, transform the data into a format that's easy to analyze, and fuse different data streams together to create a comprehensive view of the company's production output.
Table of Contents
Let's discuss each step in detail.
1. Data Cleaning and Validation
To begin the process of data transformation, the first step is to ensure that the data is clean and validated. This can be a complex task due to the large volumes of data and the variety of data sources. The cleaning process involves removing any inaccuracies, inconsistencies, or errors in the data. Validating the data involves checking it against a set of predefined rules to ensure that it is accurate, complete, and relevant. By cleaning and validating the data, we can ensure that the data engine processes high-quality data, resulting in more accurate and meaningful insights.
2. Data Pipeline Operations
After cleaning and validation, the data is transformed through a series of operations to turn it into a format that can be used for analytics. This process is known as data pipeline operation and is crucial for generating accurate and meaningful insights.
3. Fusing Data Streams for Comprehensive Insights
Once the data has been transformed, the data engine fuses different data streams or events within the data streams together. This step is important for creating a comprehensive view of the data and identifying patterns or trends that may not be visible from individual data sources.
Finally, the data engine performs analytics on the fused data to generate comprehensive events that can provide valuable insights to the organization. With the ability to process large amounts of data in real time, distributed data engines are able to provide organizations with the tools they need to make informed decisions and improve operational efficiency.
We will discuss about distributed data engines in the next section.
How a data engine simplifies complex data and enables effective analytics
As mentioned earlier, the events generated by the data engine are not the final intelligence but rather the building blocks that enable data analysts or engineers to work on the final data and intelligence. This intelligence can be operational or business intelligence, with the former being real-time and the latter based on batch or historical analytics.
The beauty of a data engine lies in its ability to handle massive amounts of operational data, which can come in with multiple data tags at high speeds.
For instance, in a manufacturing environment, there can be hundreds and thousands of data tags per system, with data types coming in every few seconds. Without a data engine, it can be very challenging to derive any meaningful insights from such data. However, by leveraging a data engine, we can reduce both the data volume and complexity, making it easier to extract deep insights and derive value from the data.
Introduction to distributed data engines
Distributed data engines are a technology that takes the power of a data engine and makes it even more potent. By deploying data engines in various locations, such as the cloud or on-premise, and scaling them to multiple instances for a particular type of data stream, you can analyze massive amounts of operational data.
For example, if you have thousands of data tags stored in Snowflake, you may not be able to process all of it in a single data engine, so you may need to split the task into multiple data engines.
By doing this, you can combine the resulting events from these data engines into comprehensive insights, allowing you to analyze hundreds of thousands of data tags to obtain insights.
The events are not the final answer, however; they are typically fed into business intelligence software to produce reports on various metrics of interest. The distributed data engines provide a scalable solution to analyzing large amounts of data and generating valuable insights.
Uses of Distributed Data Engines
In the oil and gas industry, a distributed data engine can be used to analyze the performance of multi-million dollar machines, such as mud pumps. Mud pumps have a lot of sensors and generate a large amount of data that can be analyzed to prevent downtime and improve efficiency. By deploying data engines in different locations, you can process and analyze data from these machines in real time, allowing you to quickly identify issues and take corrective actions to prevent costly downtime.
This is just one example of how distributed data engines can be used to analyze operational data and drive insights that can lead to significant improvements in efficiency and productivity.
Mud pumps generate a lot of data from various sources, including external vibration sensors that monitor if the vibrations are normal or not, electronic controlling recorders (EDR) that monitor almost every component inside the pump, work log books that are digital records of component inspections and replacements, and operational software that controls the equipment on site.
All of this data is crucial to analyzing the operation of the mud pump, preventing downtime, and improving efficiency. The distributed data engine is able to handle and analyze this massive amount of data, from all these different sources, to produce meaningful insights that can be used to optimize operations.
The data on such machines is also distributed in different locations, some of which may already be streamed into the cloud while others may still be on-site.
The purpose of analyzing this data is to look for alerts that come from any of the data streams and understand if the alerts are real or not. Often, today's systems can generate thousands of alerts per data source per day, making it impossible for an operator or driller to go through each one of them. In such cases, a data engine can be used to figure out if the alerts are real or not and reduce them into smaller numbers so that an operator or driller can handle them.
The events that we are looking for from the raw data are alerts that indicate that something has gone wrong with the machine or that it is not operating at optimal efficiency. These events are derived from the raw data by the data engine and are used to create alerts that are easily understandable by the operator or driller. By reducing the number of alerts to a manageable level, the operator or driller can focus on the critical alerts and take action to prevent downtime and improve the efficiency of the machine.
In summary, a distributed data engine allows us to analyze a huge amount of data from machines like mud pumps, which are distributed in different locations. The data engine helps us to identify real alerts and reduce the number of alerts to a manageable level, enabling the operator or driller to focus on critical alerts and take action to prevent downtime and improve efficiency.
Importance of Monitoring Stress Levels
The stress interval is a critical factor in monitoring these machines because stress can cause significant damage. By analyzing the stress levels, you can predict potential failures and identify areas that need attention. Additionally, by collecting data on all the issues from all the sites and machines, you can determine the top problems that Drillers are facing. This information can help you to develop strategies to address these issues and improve the reliability of the equipment.
Another important factor to consider is mud volume. Mud volume is a critical metric because it reflects the total throughput of the system. By understanding how the mud volume is influenced by different components and configurations, you can gain insight into how the machines are working and identify opportunities to improve efficiency. By optimizing the mud volume, you can reduce costs, increase productivity, and maximize the value of your investment in this equipment.
Simplifying BI Analytics through the Complex Process of Converting Raw Data into Events
The process of converting raw data into events is complex, but it simplifies working on BI analytics compared to working directly on raw data. Another example system is an instrumentation system where there are tens or hundreds of analytical instruments connected to a data engine that collects status data and performs analysis on it. The data source in this case includes instrument status and messages, but the events are actually tests, including information such as test runtime, status, success or failure, product, error codes, and operators. By collecting raw data and generating test events from it, you can aggregate data from multiple labs and perform BI analysis.
In this context, a distributed data engine can act as a bridge between the data sources and the BI software. The data engine can collect data from various on-premises systems and distribute it to the BI software. This way, the BI software can access all the data available in the organization regardless of where it's stored.
The data engine can also perform various transformations on the data to make it suitable for the BI software. For example, it can combine data from different sources, filter out unwanted data, or convert the data into a format that the BI software can understand.
By using a distributed data engine as a pipeline, organizations can streamline their data integration process and make it more efficient. This allows them to get insights from their data more quickly and make better decisions based on that data.
In this scenario, the data engines are deployed to various locations and are used to collect data sources through a data pipeline operation. The collected data is then sent to a data warehouse, making it available to the bi software. This approach is useful because most bi software assumes that the data is readily available, which is often not the case for operational data.
Another interesting topology is to use a data connector to gather only the necessary data into the bi software, instead of sending all data sources into a data warehouse. This approach differs from the previous scheme because, in the previous scheme, all the data is first collected into a data warehouse, which often involves collecting as much data as possible, even if it may not be needed. However, with this approach, the bi software can access all of the data sources without having to collect all the data ahead of time.
Conclusion
The distributed data engine can be a powerful tool in making data accessible and useful for enterprise operations. By connecting data sources through a data pipeline operation and sending them into a data warehouse or using a data connector to gather specific data into the BI software, organizations can gather the data they need for BI analytics.
This is particularly useful when dealing with distributed data sources that may be in different locations and networks. By converting raw data into events, organizations can easily analyze data to understand metrics such as lab throughput, efficiency, and errors, as well as operator and lab performance.
The data connectors and virtual data sources make it efficient to access and use data sources without needing to collect all data ahead of time in a data warehouse.
Overall, the distributed data engine is a powerful tool for organizations looking to gain insights from their data sources.